Imputing incomplete medical tests and predicting patient outcomes are crucial for guiding the decision making for therapy, such as after an Achilles Tendon Rupture (ATR). We formulate the problem of data imputation and prediction for ATR relevant medical measurements into a recommender system framework. By applying MatchBox, which is a collaborative filtering approach, on a real dataset collected from 374 ATR patients, we aim at offering personalized medical data imputation and prediction. In this work, we show the feasibility of this approach and discuss potential research directions by conducting initial qualitative evaluations.
@article{arxiv.1612.02490,
title = {Bridging Medical Data Inference to Achilles Tendon Rupture Rehabilitation},
author = {An Qu and Cheng Zhang and Paul Ackermann and Hedvig Kjellström},
journal= {arXiv preprint arXiv:1612.02490},
year = {2016}
}
Comments
Workshop on Machine Learning for Healthcare, NIPS 2016, Barcelona, Spain